1. Field of the Invention
This invention relates to methods and apparatus for forecasting litigation discovery costs by collecting and analyzing historic data to predict future costs and timing based on interpolation of historic event patterns.
2. Prior Art
Because of the increasing cost of litigation discovery, litigation expenses are increasing in both absolute dollars and as a percentage of operating budgets for some companies. It is difficult to predict discovery costs on a matter-by-matter basis when the outcome of any individual litigation matter cannot be accurately predicted. The amount and timing of discovery expenses can have a material impact on a company's operating results.
Previously, forecasting methods for E*Discovery costs were very ad hoc and manual. Only limited data could be leveraged because there was no effective means to collect and mine historical data and no effective way to track detailed recent activity on current matters. As a result, forecasts were made using empirical forecasting methods that were based more often on perception of cost trends rather than on real data, using simple models that were implemented using manual spreadsheet formulas. Consistency and accuracy was extremely low. As a result, such forecasts were not relied upon for budgeting purposes. Instead, budgets were developed using simple year-to-year trends combined with intuitive guesses.
Given the volume of current litigation in large corporations, the number of people possessing information related to each matter in litigation, and the widespread use of third party contractors, who provide discovery services, it is difficult to develop and maintain accurate cost forecasts without a dedicated cost-forecasting tool. To enable companies to more accurately an effectively forecast their legal expenses, automated methodology and process for predicting discovery costs is needed. Important attributes of an effective model for forecasting discovery costs are ease of use, flexibility, and data integrity. The model should enable a person with little or no training in finance to produce a forecast that he/she is confident delivering to the company's management team. The key to having confidence in the forecast is knowing that the data used to create the forecast is complete and specific to the company and was collected in a way that minimizes the risk of human error.
The concept of a particular litigation matter moving through a particular series of sequential stages may not be entirely sufficient for modeling more sophisticated business litigation processes. For example, it is desirable that a second request with collections be created at any time, not necessarily after a particular stage. Ad-hoc collection logs may need to be created before a collection notice is created. These out-of-order events increase the probability of collection but are not properly modeled using sequential litigation stages model.